Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data

Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data

Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have...

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Bibliographic Details
Main Author: Hilal Arslan
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Proceedings
Subjects:
coronavirus
COVID-19
machine learning
CpG islands
Online Access:https://www.mdpi.com/2504-3900/74/1/20
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spelling doaj-eea97963327a4f6389ad030845ef2eba2021-03-17T00:00:47ZengMDPI AGProceedings2504-39002021-03-0174202010.3390/proceedings2021074020Machine Learning Methods for COVID-19 Prediction Using Human Genomic DataHilal Arslan0Department of Computer Engineering, Faculty of Engineering and Architecture, Izmir Bakırçay University, Izmir 35665, TurkeyAccurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy.https://www.mdpi.com/2504-3900/74/1/20coronavirusCOVID-19machine learningCpG islands
collection DOAJ
language English
format Article
sources DOAJ
author Hilal Arslan
spellingShingle Hilal Arslan
Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
Proceedings
coronavirus
COVID-19
machine learning
CpG islands
author_facet Hilal Arslan
author_sort Hilal Arslan
title Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
title_short Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
title_full Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
title_fullStr Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
title_full_unstemmed Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
title_sort machine learning methods for covid-19 prediction using human genomic data
publisher MDPI AG
series Proceedings
issn 2504-3900
publishDate 2021-03-01
description Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy.
topic coronavirus
COVID-19
machine learning
CpG islands
url https://www.mdpi.com/2504-3900/74/1/20
work_keys_str_mv AT hilalarslan machinelearningmethodsforcovid19predictionusinghumangenomicdata
_version_ 1724219264476381184

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